Philip Kalikman

I am a University Assistant Professor of Real Estate, Finance, and Economics at the University of Cambridge, Department of Land Economy, in Cambridge UK. I research several areas of real estate and finance including mortgage default and prepayment, housing market dynamics, macroprudential policy, and financial crises. I build and study structural, heterogeneous, computational, and AI/ML models. I am interested in the interactions among real estate, regulation, and racial discrimination, and in how these affect equality and opportunity. I received my MA, MPhil, and PhD in Economics from Yale University and my BA in pure Mathematics from the University of Chicago.

Prior to completing my PhD, I worked at a real estate hedge fund, as a consulting economic policy advisor to Secretary of State Hillary Clinton and members of the U.S. Senate, and in fintech venture capital with a former U.S. Under Secretary of the Treasury and U.S. Comptroller of the Currency. I continue to advise fintech startups, venture capital firms, hedge funds, and real estate firms. I also serve as Treasurer on the boards of Students for Educational Justice, a youth-led nonprofit driving efforts for racial justice in Connecticut, and of New York Festival of Song, a performing arts organization in New York City. I like to cook, read, make and listen to music, dance, travel, solve puzzles, spend time with my little brother and sister, and research the economics of coffee and taco consumption. (Preliminary results are encouraging.)

Research

Upcoming Conferences

I will present at the following upcoming conferences; please say hello if you see me there: Vietnam Symposium in Banking and Finance, October 2023; Sustainable Finance Innovation Centre Annual Conference, November 2023; and ASSA–AREUEA Annual Meeting, for which I also serve as a member of the Scientific Committee, January 2024.

Projects

Underlined titles link to working and published papers; arrows reveal abstracts.

"Targeted Principal Forgiveness Is Effective: Mortgage Modification and Financial Crisis," with Joelle Scally (2023). Semifinalist for Best Paper Award, Financial Markets & Institutions, FMA 2022.
Research into the Global Financial Crisis finds forgiving mortgage principal ineffective at stemming defaults; authors argue that borrowers default because of illiquidity, not strategically. We argue the opposite. Targeted forgiveness is effective. And default is better explained by quantifying how illiquidity and strategy interact. We embed their interactions in a computational heterogeneous structural model. We introduce idiosyncratic default penalties: differing penalties underpin borrowers' differing deviations from pure-financial optimality. We run the model with heterogeneous microdata, estimating penalties from credit scores and payment histories. Forgiving low-score, deep-underwater borrowers would have eliminated nearly all their defaults, with net gain for lenders.
"Mortgage Default: A Heterogeneous-Agent Model," with Joelle Scally (2023).
We introduce a loan-level model of mortgage default with heterogeneity in borrower characteristics and mortgage terms, including idiosyncratic penalties for default. Borrowers’ penalties determine how closely their behavior hews to the predictions of the double-trigger or strategic models. The state space varies loan-to-loan based on all of the loan’s, borrower's, property's, and neighborhood's idiosyncratic characteristics. We test the model on a high-performance computing cluster against real data drawn from linked databases with billions of observations of hundreds of simultaneous attributes. The model predicts defaults out-of-sample, fits cross-sectional characteristics of the distribution of mortgage performance, and classifies likelihood of default with high accuracy and better than all known benchmarks.
"Machine Learning and Mortgage Lending" (Grant-funded work in progress).
Banks and fintech lenders increasingly rely on computer-aided models in lending decisions. Traditional models were interpretable: decisions were based on observable factors, such as whether a borrower’s credit score was above a threshold value, and explainable in terms of combinations of these factors. By contrast, modern machine learning models are opaque and non-interpretable. Their opaqueness and reliance on historical data that is the artifact of past racial discrimination means these new models risk embedding and exacerbating such discrimination, even if lenders do not intend to discriminate. We aim to develop a framework for interpreting machine learning mortgage lending models and testing them for discrimination. We will use Explainable Artificial Intelligence models to characterize what features drive the decisions produced by calibrated ML lending models and to develop a framework for black-box testing new models for discrimination. We expect our findings to bear on the regulation of model use in mortgage lending, and expect our framework to provide the means for regulators to monitor and enforce compliance with anti-discrimination laws.
"Climate Change and Financial Assets: The Effects of Stormwater on Mortgage Default", with Hai Long Duong &c.
We establish that climate change poses risks to financial assets. We exploit a unique dataset with high-resolution data on adverse weather. Using a difference-in-difference approach, we identify the effect of stormwater runoff on mortgage default. Residential property owners inadequately self-insure against flood risk from stormwater runoff, leading mortgages backed by properties in areas exposed to increased runoff to go delinquent and default at higher rates. Moderate storms, not just headline disasters, exacerbate mortgage default. Losses are subsidized by borrowers in other geographies, but discrimination and redlining considerations prevent the GSEs from correcting the mispricing in loan-level price adjustments. Losses are projected to grow substantially in the next several decades.
"Credit Availability Did Expand Before the Global Financial Crisis" (2016).
Scholars remain divided on whether the Global Financial Crisis was fueled by significantly looser credit underwriting standards in the early 2000s, disagreeing not only about whether loose standards caused the crisis, but even on whether standards were loose in that era. I examine three different loan-level mortgage origination datasets in the US to try to disentangle why scholars disagree on this question. I show that linking loans at the property-level, which is only possible for some of those datasets, is necessary to see the higher origination loan-to-value ratios that obtained before the crisis. Moreover, I show that leverage rose especially for less creditworthy borrowers, many of whom would have been excluded entirely from getting a mortgage in times of tighter credit. The simultaneous expansion in low-LTV financing diluted average leverage, obscuring the reality that unprecedented cheap credit was available pre-Crisis.
"Endogenous Leverage and Credit in an Agent-Based Model of the Housing Market," with John Geanakoplos, Ravi Jagadeesan, Emily Dodwell, and Jesse Wang (2015).
Did house prices rise before the Global Financial Crisis because lenders expanded credit? Or did lenders expand credit because of the economic forces that led prices to rise? We model endogenous credit extension in a computational agent-based model of the housing market. Profit-maximizing, rational, forward-looking lenders charge interest rates to borrowers with different loan-to-value ratios and different credit scores based on the lenders' estimates of those borrowers' propensities to default. When lenders extrapolate future default rates from prior history, then expanding house prices shield borrowers from negative equity and fuel an artificially low level of defaults. Lenders respond by loosening credit standards, which feeds back into borrowers' demand for housing, fueling a fragile bubble. The bubble bursts when relatively few initial defaults cascade, leading lenders to choke off further credit and subsequent demand for housing to dry up. The model captures the time series and distributional features of lending and house prices observed throughout the Global Financial Crisis and provides a micro-foundation for studying how credit availability fuels bubbles.
"An Agent-Based Model of the Housing Market Bubble in Metropolitan Washington DC," with Doyne Farmer, John Geanakoplos, Peter Howitt, et al. (2014).
With Robert Axtell, Benjamin Conlee, Ernesto Carella, Doyne Farmer, John Geanakoplos, Jon Goldstein, Matthew Hendrey, Peter Howitt, David Masad, and Nathan Palmer. Published in Housing markets and the macroeconomy: challenges for monetary policy and financial stability---a conference by Deutsche Bundesbank, the German Research Foundation (DFG) and the International Monetary Fund. We develop a computational model of a regional housing market. Over a million distinct agents buy, sell, and rent houses according to different behavior rules, which depend on demographic, financial, and housing stock characteristics we estimate using data in the Washington, D.C. metropolitan area from 1997 -- 2009. We use both individual record-level matching and statistical inference on several dozen disparate datasets to simulate a single joint distribution of household characteristics. Households' transactions endogenously generate a housing market bubble and crash that resembles the observed history not only in the timing and magnitude of the boom and bust in home prices, but also in other aggregate dynamics such as time-on-market, homeownership rate, and vacancy rate and in distributional characteristics such as house prices across tiers of building quality and loan performance across bands of credit quality. We use the model to study the drivers of the bubble. We show that low risk-free interest rates do not generate a house price bubble when credit availability is restricted, whereas loose credit contributes to a bubble even without low